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Modeling of the ARMA random effects covariance matrix in logistic random effects models
- Modeling of the ARMA random effects covariance matrix in logistic random effects models
- Lee, Keunbaik; Jung, Hoimin; Yoo, Jae Keun
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- STATISTICAL METHODS AND APPLICATIONS
- STATISTICAL METHODS AND APPLICATIONS vol. 28, no. 2, pp. 281 - 299
- Cholesky decomposition; Longitudinal data; Heteroscedastic; Repeated outcomes
- SPRINGER HEIDELBERG
- SCIE; SCOPUS
- Document Type
- Logistic random effects models (LREMs) have been frequently used to analyze longitudinal binary data. When a random effects covariance matrix is used to make proper inferences on covariate effects, the random effects in the models account for both within-subject association and between-subject variation, but the covariance matix is difficult to estimate because it is high-dimensional and should be positive definite. To overcome these limitations, two Cholesky decomposition approaches were proposed for precision matrix and covariance matrix: modified Cholesky decomposition and moving average Cholesky decomposition, respectively. However, the two approaches may not work when there are non-trivial and complicated correlations of repeated outcomes. In this paper, we combined the two decomposition approaches to model the random effects covariance matrix in the LREMs, thereby capturing a wider class of sophisticated dependence structures while achieving parsimony in parametrization. We then used our proposed model to analyze lung cancer data.
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